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RESIDUAL RECURRENT NEURAL NETWORK FOR SPEECH ENHANCEMENT
Most current speech enhancement models use spectrogram features that require an expensive transformation and result in phase information loss. Previous work has overcome these issues by using convolutional networks to learn the temporal correlations across high-resolution waveforms. These models, ho...
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| Publicado no: | Proc IEEE Int Conf Acoust Speech Signal Process |
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| Main Authors: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7954533/ https://ncbi.nlm.nih.gov/pubmed/33716575 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/icassp40776.2020.9053544 |
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